In [1]:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
In [2]:
netflix = pd.read_csv('netflix_titles.csv')
netflix.head()
Out[2]:
show_id type title director cast country date_added release_year rating duration listed_in description
0 81145628 Movie Norm of the North: King Sized Adventure Richard Finn, Tim Maltby Alan Marriott, Andrew Toth, Brian Dobson, Cole... United States, India, South Korea, China September 9, 2019 2019 TV-PG 90 min Children & Family Movies, Comedies Before planning an awesome wedding for his gra...
1 80117401 Movie Jandino: Whatever it Takes NaN Jandino Asporaat United Kingdom September 9, 2016 2016 TV-MA 94 min Stand-Up Comedy Jandino Asporaat riffs on the challenges of ra...
2 70234439 TV Show Transformers Prime NaN Peter Cullen, Sumalee Montano, Frank Welker, J... United States September 8, 2018 2013 TV-Y7-FV 1 Season Kids' TV With the help of three human allies, the Autob...
3 80058654 TV Show Transformers: Robots in Disguise NaN Will Friedle, Darren Criss, Constance Zimmer, ... United States September 8, 2018 2016 TV-Y7 1 Season Kids' TV When a prison ship crash unleashes hundreds of...
4 80125979 Movie #realityhigh Fernando Lebrija Nesta Cooper, Kate Walsh, John Michael Higgins... United States September 8, 2017 2017 TV-14 99 min Comedies When nerdy high schooler Dani finally attracts...
In [3]:
netflix_shows = netflix[netflix['type']=='TV Show']
netflix_movies = netflix[netflix['type']=='Movie']
In [4]:
netflix_movies
Out[4]:
show_id type title director cast country date_added release_year rating duration listed_in description
0 81145628 Movie Norm of the North: King Sized Adventure Richard Finn, Tim Maltby Alan Marriott, Andrew Toth, Brian Dobson, Cole... United States, India, South Korea, China September 9, 2019 2019 TV-PG 90 min Children & Family Movies, Comedies Before planning an awesome wedding for his gra...
1 80117401 Movie Jandino: Whatever it Takes NaN Jandino Asporaat United Kingdom September 9, 2016 2016 TV-MA 94 min Stand-Up Comedy Jandino Asporaat riffs on the challenges of ra...
4 80125979 Movie #realityhigh Fernando Lebrija Nesta Cooper, Kate Walsh, John Michael Higgins... United States September 8, 2017 2017 TV-14 99 min Comedies When nerdy high schooler Dani finally attracts...
6 70304989 Movie Automata Gabe Ibáñez Antonio Banderas, Dylan McDermott, Melanie Gri... Bulgaria, United States, Spain, Canada September 8, 2017 2014 R 110 min International Movies, Sci-Fi & Fantasy, Thrillers In a dystopian future, an insurance adjuster f...
7 80164077 Movie Fabrizio Copano: Solo pienso en mi Rodrigo Toro, Francisco Schultz Fabrizio Copano Chile September 8, 2017 2017 TV-MA 60 min Stand-Up Comedy Fabrizio Copano takes audience participation t...
... ... ... ... ... ... ... ... ... ... ... ... ...
5577 80085438 Movie Frank and Cindy G.J. Echternkamp NaN United States April 1, 2016 2007 TV-MA 70 min Documentaries Frank was a rising pop star when he married Ci...
5578 80085439 Movie Frank and Cindy G.J. Echternkamp Rene Russo, Oliver Platt, Johnny Simmons, Jane... United States April 1, 2016 2015 R 102 min Comedies, Dramas, Independent Movies A student filmmaker vengefully turns his camer...
5579 80011846 Movie Iverson Zatella Beatty Allen Iverson United States April 1, 2016 2014 NR 88 min Documentaries, Sports Movies This unfiltered documentary follows the rocky ...
5580 80064521 Movie Jeremy Scott: The People's Designer Vlad Yudin Jeremy Scott United States April 1, 2016 2015 PG-13 109 min Documentaries The journey of fashion designer Jeremy Scott f...
6231 80116008 Movie Little Baby Bum: Nursery Rhyme Friends NaN NaN NaN NaN 2016 NaN 60 min Movies Nursery rhymes and original music for children...

4265 rows × 12 columns

In [5]:
netflix_shows
Out[5]:
show_id type title director cast country date_added release_year rating duration listed_in description
2 70234439 TV Show Transformers Prime NaN Peter Cullen, Sumalee Montano, Frank Welker, J... United States September 8, 2018 2013 TV-Y7-FV 1 Season Kids' TV With the help of three human allies, the Autob...
3 80058654 TV Show Transformers: Robots in Disguise NaN Will Friedle, Darren Criss, Constance Zimmer, ... United States September 8, 2018 2016 TV-Y7 1 Season Kids' TV When a prison ship crash unleashes hundreds of...
5 80163890 TV Show Apaches NaN Alberto Ammann, Eloy Azorín, Verónica Echegui,... Spain September 8, 2017 2016 TV-MA 1 Season Crime TV Shows, International TV Shows, Spanis... A young journalist is forced into a life of cr...
8 80117902 TV Show Fire Chasers NaN NaN United States September 8, 2017 2017 TV-MA 1 Season Docuseries, Science & Nature TV As California's 2016 fire season rages, brave ...
26 80244601 TV Show Castle of Stars NaN Chaiyapol Pupart, Jintanutda Lummakanon, Worra... NaN September 7, 2018 2015 TV-14 1 Season International TV Shows, Romantic TV Shows, TV ... As four couples with different lifestyles go t...
... ... ... ... ... ... ... ... ... ... ... ... ...
6228 80159925 TV Show Kikoriki NaN Igor Dmitriev NaN NaN 2010 TV-Y 2 Seasons Kids' TV A wacky rabbit and his gang of animal pals hav...
6229 80000063 TV Show Red vs. Blue NaN Burnie Burns, Jason Saldaña, Gustavo Sorola, G... United States NaN 2015 NR 13 Seasons TV Action & Adventure, TV Comedies, TV Sci-Fi ... This parody of first-person shooter games, mil...
6230 70286564 TV Show Maron NaN Marc Maron, Judd Hirsch, Josh Brener, Nora Zeh... United States NaN 2016 TV-MA 4 Seasons TV Comedies Marc Maron stars as Marc Maron, who interviews...
6232 70281022 TV Show A Young Doctor's Notebook and Other Stories NaN Daniel Radcliffe, Jon Hamm, Adam Godley, Chris... United Kingdom NaN 2013 TV-MA 2 Seasons British TV Shows, TV Comedies, TV Dramas Set during the Russian Revolution, this comic ...
6233 70153404 TV Show Friends NaN Jennifer Aniston, Courteney Cox, Lisa Kudrow, ... United States NaN 2003 TV-14 10 Seasons Classic & Cult TV, TV Comedies This hit sitcom follows the merry misadventure...

1969 rows × 12 columns

In [6]:
sns.set_style('whitegrid')

sns.countplot(x='type',data=netflix)
Out[6]:
<matplotlib.axes._subplots.AxesSubplot at 0x20fb439e608>
In [7]:
netflix_date = netflix_shows[['date_added']].dropna()
In [8]:
netflix_date
Out[8]:
date_added
2 September 8, 2018
3 September 8, 2018
5 September 8, 2017
8 September 8, 2017
26 September 7, 2018
... ...
6218 April 10, 2019
6219 April 1, 2019
6220 April 1, 2016
6221 April 1, 2016
6222 April 1, 2014

1959 rows × 1 columns

In [10]:
netflix_date['year'] = netflix_date['date_added'].apply(lambda x : x.split(',')[-1])
In [11]:
netflix_date
Out[11]:
date_added year
2 September 8, 2018 2018
3 September 8, 2018 2018
5 September 8, 2017 2017
8 September 8, 2017 2017
26 September 7, 2018 2018
... ... ...
6218 April 10, 2019 2019
6219 April 1, 2019 2019
6220 April 1, 2016 2016
6221 April 1, 2016 2016
6222 April 1, 2014 2014

1959 rows × 2 columns

In [12]:
netflix_date['month'] = netflix_date['date_added'].apply(lambda x : x.lstrip().split(' ')[0])
In [13]:
netflix_date
Out[13]:
date_added year month
2 September 8, 2018 2018 September
3 September 8, 2018 2018 September
5 September 8, 2017 2017 September
8 September 8, 2017 2017 September
26 September 7, 2018 2018 September
... ... ... ...
6218 April 10, 2019 2019 April
6219 April 1, 2019 2019 April
6220 April 1, 2016 2016 April
6221 April 1, 2016 2016 April
6222 April 1, 2014 2014 April

1959 rows × 3 columns

In [14]:
month_order = ['January', 'February', 'March', 'April', 'May', 'June', 'July', 'August', 'September', 'October', 'November', 'December'][::-1]
In [15]:
netflix_date.groupby('year')['month'].value_counts()
Out[15]:
year   month   
 2008  February     1
 2012  August       1
       July         1
       October      1
 2013  October      2
                   ..
 2019  February    59
       April       58
       December    53
       January     44
 2020  January     37
Name: month, Length: 72, dtype: int64
In [16]:
netflix_date.groupby('year')['month'].value_counts().unstack()
Out[16]:
month April August December February January July June March May November October September
year
2008 NaN NaN NaN 1.0 NaN NaN NaN NaN NaN NaN NaN NaN
2012 NaN 1.0 NaN NaN NaN 1.0 NaN NaN NaN NaN 1.0 NaN
2013 1.0 1.0 NaN NaN NaN NaN NaN 1.0 NaN NaN 2.0 1.0
2014 1.0 NaN 1.0 1.0 NaN NaN NaN NaN NaN 3.0 NaN NaN
2015 6.0 NaN 7.0 1.0 NaN 3.0 3.0 2.0 2.0 2.0 5.0 1.0
2016 8.0 19.0 44.0 7.0 29.0 12.0 8.0 4.0 5.0 18.0 19.0 19.0
2017 31.0 41.0 43.0 19.0 15.0 35.0 30.0 39.0 26.0 33.0 39.0 36.0
2018 34.0 42.0 75.0 27.0 27.0 32.0 31.0 40.0 32.0 48.0 55.0 49.0
2019 58.0 71.0 53.0 59.0 44.0 78.0 61.0 74.0 68.0 87.0 84.0 66.0
2020 NaN NaN NaN NaN 37.0 NaN NaN NaN NaN NaN NaN NaN
In [17]:
netflix_date.groupby('year')['month'].value_counts().unstack().fillna(0)
Out[17]:
month April August December February January July June March May November October September
year
2008 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
2012 0.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
2013 1.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 2.0 1.0
2014 1.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 3.0 0.0 0.0
2015 6.0 0.0 7.0 1.0 0.0 3.0 3.0 2.0 2.0 2.0 5.0 1.0
2016 8.0 19.0 44.0 7.0 29.0 12.0 8.0 4.0 5.0 18.0 19.0 19.0
2017 31.0 41.0 43.0 19.0 15.0 35.0 30.0 39.0 26.0 33.0 39.0 36.0
2018 34.0 42.0 75.0 27.0 27.0 32.0 31.0 40.0 32.0 48.0 55.0 49.0
2019 58.0 71.0 53.0 59.0 44.0 78.0 61.0 74.0 68.0 87.0 84.0 66.0
2020 0.0 0.0 0.0 0.0 37.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
In [19]:
netflix_date.groupby('year')['month'].value_counts().unstack().fillna(0)[month_order].T
Out[19]:
year 2008 2012 2013 2014 2015 2016 2017 2018 2019 2020
month
December 0.0 0.0 0.0 1.0 7.0 44.0 43.0 75.0 53.0 0.0
November 0.0 0.0 0.0 3.0 2.0 18.0 33.0 48.0 87.0 0.0
October 0.0 1.0 2.0 0.0 5.0 19.0 39.0 55.0 84.0 0.0
September 0.0 0.0 1.0 0.0 1.0 19.0 36.0 49.0 66.0 0.0
August 0.0 1.0 1.0 0.0 0.0 19.0 41.0 42.0 71.0 0.0
July 0.0 1.0 0.0 0.0 3.0 12.0 35.0 32.0 78.0 0.0
June 0.0 0.0 0.0 0.0 3.0 8.0 30.0 31.0 61.0 0.0
May 0.0 0.0 0.0 0.0 2.0 5.0 26.0 32.0 68.0 0.0
April 0.0 0.0 1.0 1.0 6.0 8.0 31.0 34.0 58.0 0.0
March 0.0 0.0 1.0 0.0 2.0 4.0 39.0 40.0 74.0 0.0
February 1.0 0.0 0.0 1.0 1.0 7.0 19.0 27.0 59.0 0.0
January 0.0 0.0 0.0 0.0 0.0 29.0 15.0 27.0 44.0 37.0
In [20]:
df = netflix_date.groupby('year')['month'].value_counts().unstack().fillna(0)[month_order].T
In [23]:
plt.figure(figsize=(12,7))

plt.pcolor(df,cmap='viridis',edgecolors='black',lw=2)
Out[23]:
<matplotlib.collections.PolyCollection at 0x20fb45c9188>
In [32]:
plt.figure(figsize=(20,20))
sns.heatmap(df,cmap='coolwarm',linecolor='white',linewidths=2)
Out[32]:
<matplotlib.axes._subplots.AxesSubplot at 0x20fb496bf48>
In [34]:
plt.figure(figsize=(12,7))
sns.countplot(x='rating',data=netflix_movies)
Out[34]:
<matplotlib.axes._subplots.AxesSubplot at 0x20fb59a6f08>
In [35]:
imdb = pd.read_csv('IMDB-Movie-Data.csv')
imdb.head()
Out[35]:
Rank Title Genre Description Director Actors Year Runtime (Minutes) Rating Votes Revenue (Millions) Metascore
0 1 Guardians of the Galaxy Action,Adventure,Sci-Fi A group of intergalactic criminals are forced ... James Gunn Chris Pratt, Vin Diesel, Bradley Cooper, Zoe S... 2014 121 8.1 757074 333.13 76.0
1 2 Prometheus Adventure,Mystery,Sci-Fi Following clues to the origin of mankind, a te... Ridley Scott Noomi Rapace, Logan Marshall-Green, Michael Fa... 2012 124 7.0 485820 126.46 65.0
2 3 Split Horror,Thriller Three girls are kidnapped by a man with a diag... M. Night Shyamalan James McAvoy, Anya Taylor-Joy, Haley Lu Richar... 2016 117 7.3 157606 138.12 62.0
3 4 Sing Animation,Comedy,Family In a city of humanoid animals, a hustling thea... Christophe Lourdelet Matthew McConaughey,Reese Witherspoon, Seth Ma... 2016 108 7.2 60545 270.32 59.0
4 5 Suicide Squad Action,Adventure,Fantasy A secret government agency recruits some of th... David Ayer Will Smith, Jared Leto, Margot Robbie, Viola D... 2016 123 6.2 393727 325.02 40.0
In [46]:
imdb_rating = imdb[['Rating']]
In [47]:
imdb_rating
Out[47]:
Rating
0 8.1
1 7.0
2 7.3
3 7.2
4 6.2
... ...
995 6.2
996 5.5
997 6.2
998 5.6
999 5.3

1000 rows × 1 columns

In [38]:
imbd_titles = imdb[['Title','Year','Genre']]
In [39]:
imbd_titles
Out[39]:
Title Year Genre
0 Guardians of the Galaxy 2014 Action,Adventure,Sci-Fi
1 Prometheus 2012 Adventure,Mystery,Sci-Fi
2 Split 2016 Horror,Thriller
3 Sing 2016 Animation,Comedy,Family
4 Suicide Squad 2016 Action,Adventure,Fantasy
... ... ... ...
995 Secret in Their Eyes 2015 Crime,Drama,Mystery
996 Hostel: Part II 2007 Horror
997 Step Up 2: The Streets 2008 Drama,Music,Romance
998 Search Party 2014 Adventure,Comedy
999 Nine Lives 2016 Comedy,Family,Fantasy

1000 rows × 3 columns

In [50]:
ratings = pd.DataFrame({'Title':imbd_titles['Title'],
                       'Release Year':imbd_titles['Year'],
                       'Rating':imdb_rating['Rating'],
                       'Genre':imbd_titles['Genre']})
In [51]:
ratings
Out[51]:
Title Release Year Rating Genre
0 Guardians of the Galaxy 2014 8.1 Action,Adventure,Sci-Fi
1 Prometheus 2012 7.0 Adventure,Mystery,Sci-Fi
2 Split 2016 7.3 Horror,Thriller
3 Sing 2016 7.2 Animation,Comedy,Family
4 Suicide Squad 2016 6.2 Action,Adventure,Fantasy
... ... ... ... ...
995 Secret in Their Eyes 2015 6.2 Crime,Drama,Mystery
996 Hostel: Part II 2007 5.5 Horror
997 Step Up 2: The Streets 2008 6.2 Drama,Music,Romance
998 Search Party 2014 5.6 Adventure,Comedy
999 Nine Lives 2016 5.3 Comedy,Family,Fantasy

1000 rows × 4 columns

In [52]:
ratings.columns
Out[52]:
Index(['Title', 'Release Year', 'Rating', 'Genre'], dtype='object')
In [54]:
ratings.drop_duplicates(subset=['Title','Release Year','Rating'],inplace=True)
In [55]:
ratings
Out[55]:
Title Release Year Rating Genre
0 Guardians of the Galaxy 2014 8.1 Action,Adventure,Sci-Fi
1 Prometheus 2012 7.0 Adventure,Mystery,Sci-Fi
2 Split 2016 7.3 Horror,Thriller
3 Sing 2016 7.2 Animation,Comedy,Family
4 Suicide Squad 2016 6.2 Action,Adventure,Fantasy
... ... ... ... ...
995 Secret in Their Eyes 2015 6.2 Crime,Drama,Mystery
996 Hostel: Part II 2007 5.5 Horror
997 Step Up 2: The Streets 2008 6.2 Drama,Music,Romance
998 Search Party 2014 5.6 Adventure,Comedy
999 Nine Lives 2016 5.3 Comedy,Family,Fantasy

1000 rows × 4 columns

In [56]:
ratings.isnull().sum()
Out[56]:
Title           0
Release Year    0
Rating          0
Genre           0
dtype: int64
In [57]:
ratings.dropna()
Out[57]:
Title Release Year Rating Genre
0 Guardians of the Galaxy 2014 8.1 Action,Adventure,Sci-Fi
1 Prometheus 2012 7.0 Adventure,Mystery,Sci-Fi
2 Split 2016 7.3 Horror,Thriller
3 Sing 2016 7.2 Animation,Comedy,Family
4 Suicide Squad 2016 6.2 Action,Adventure,Fantasy
... ... ... ... ...
995 Secret in Their Eyes 2015 6.2 Crime,Drama,Mystery
996 Hostel: Part II 2007 5.5 Horror
997 Step Up 2: The Streets 2008 6.2 Drama,Music,Romance
998 Search Party 2014 5.6 Adventure,Comedy
999 Nine Lives 2016 5.3 Comedy,Family,Fantasy

1000 rows × 4 columns

In [58]:
joint_data = ratings.merge(netflix,left_on='Title',right_on='title',how='inner')
In [59]:
joint_data
Out[59]:
Title Release Year Rating Genre show_id type title director cast country date_added release_year rating duration listed_in description
0 Mindhorn 2016 6.4 Comedy 80157866 Movie Mindhorn Sean Foley Julian Barratt, Andrea Riseborough, Essie Davi... United Kingdom May 12, 2017 2017 TV-MA 88 min Comedies, Cult Movies, Independent Movies When a twisted killer tells the police he'll o...
1 Moonlight 2016 7.5 Drama 80121348 Movie Moonlight Barry Jenkins Trevante Rhodes, André Holland, Janelle Monáe,... United States May 21, 2019 2016 R 111 min Dramas, Independent Movies, LGBTQ Movies In a crime-infested Miami neighborhood, a gay ...
2 Fallen 2016 5.6 Adventure,Drama,Fantasy 1192866 Movie Fallen Gregory Hoblit Denzel Washington, John Goodman, Donald Suther... United States November 1, 2019 1998 R 124 min Thrillers A tough homicide cop faces his most dangerous ...
3 The Last Face 2016 3.7 Drama 80115030 Movie The Last Face Sean Penn Javier Bardem, Charlize Theron, Adèle Exarchop... United States January 13, 2020 2016 R 131 min Dramas Savage civil war and a dispute over humanitari...
4 The Autopsy of Jane Doe 2016 6.8 Horror,Mystery,Thriller 80022613 Movie The Autopsy of Jane Doe André Øvredal Emile Hirsch, Brian Cox, Ophelia Lovibond, Mic... United Kingdom, United States December 30, 2018 2016 R 86 min Horror Movies, Independent Movies, Thrillers A father-son team of small-town coroners perfo...
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
195 Across the Universe 2007 7.4 Drama,Fantasy,Musical 70045863 Movie Across the Universe Julie Taymor Evan Rachel Wood, Jim Sturgess, Joe Anderson, ... United States, United Kingdom January 1, 2019 2007 PG-13 133 min Dramas, Music & Musicals, Romantic Movies An American girl and a British lad fall in lov...
196 Taare Zameen Par 2007 8.5 Drama,Family,Music 70087087 Movie Taare Zameen Par Aamir Khan Aamir Khan, Darsheel Safary, Tanay Chheda, Tis... India December 8, 2017 2007 PG 162 min Dramas, International Movies When daydreamer Ishaan is sent to boarding sch...
197 Take Me Home Tonight 2011 6.3 Comedy,Drama,Romance 70117577 Movie Take Me Home Tonight Michael Dowse Topher Grace, Anna Faris, Dan Fogler, Teresa P... United States, Germany May 16, 2019 2011 R 97 min Comedies, Romantic Movies Set in the financial boom of the late 1980s, t...
198 Resident Evil: Afterlife 2010 5.9 Action,Adventure,Horror 70128695 Movie Resident Evil: Afterlife Paul W.S. Anderson Milla Jovovich, Ali Larter, Kim Coates, Shawn ... Germany, France, United States, Canada, United... January 1, 2020 2010 R 97 min Action & Adventure, Horror Movies, Sci-Fi & Fa... The Undead Apocalypse continues as super-soldi...
199 Secret in Their Eyes 2015 6.2 Crime,Drama,Mystery 80049281 Movie Secret in Their Eyes Billy Ray Chiwetel Ejiofor, Nicole Kidman, Julia Roberts... United States, United Kingdom, Spain, South Korea April 1, 2018 2015 PG-13 111 min Dramas, Thrillers A former FBI investigator reopens the haunting...

200 rows × 16 columns

In [60]:
joint_data = joint_data.sort_values(by='Rating',ascending=False)
In [61]:
joint_data
Out[61]:
Title Release Year Rating Genre show_id type title director cast country date_added release_year rating duration listed_in description
16 Dangal 2016 8.8 Action,Biography,Drama 80166185 Movie Dangal Nitesh Tiwari Aamir Khan, Sakshi Tanwar, Fatima Sana Shaikh,... India June 21, 2017 2016 TV-PG 161 min Dramas, International Movies, Sports Movies A once-promising wrestler pursues the gold med...
8 Inception 2010 8.8 Action,Adventure,Sci-Fi 70131314 Movie Inception Christopher Nolan Leonardo DiCaprio, Joseph Gordon-Levitt, Ellen... United States, United Kingdom January 1, 2020 2010 PG-13 148 min Action & Adventure, Sci-Fi & Fantasy, Thrillers In this mind-bending sci-fi thriller, a man ru...
196 Taare Zameen Par 2007 8.5 Drama,Family,Music 70087087 Movie Taare Zameen Par Aamir Khan Aamir Khan, Darsheel Safary, Tanay Chheda, Tis... India December 8, 2017 2007 PG 162 min Dramas, International Movies When daydreamer Ishaan is sent to boarding sch...
83 The Lives of Others 2006 8.5 Drama,Thriller 70056425 Movie The Lives of Others Florian Henckel von Donnersmarck Ulrich Mühe, Martina Gedeck, Sebastian Koch, U... Germany March 15, 2019 2006 R 138 min Dramas, International Movies, Thrillers As a secret police agent eavesdrops on a succe...
11 The Departed 2006 8.5 Crime,Drama,Thriller 70044689 Movie The Departed Martin Scorsese Leonardo DiCaprio, Matt Damon, Jack Nicholson,... United States December 1, 2019 2006 R 151 min Dramas, Thrillers Two rookie Boston cops are sent deep undercove...
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
90 Movie 43 2013 4.3 Comedy,Romance 70222860 Movie Movie 43 Peter Farrelly, Will Graham, Steve Carr, Griff... Greg Kinnear, Dennis Quaid, Common, Seth MacFa... United States April 9, 2019 2013 R 94 min Comedies An eye-popping cast stars in this sketch-comed...
117 2307: Winter's Dream 2016 4.0 Sci-Fi 80184973 Movie 2307: Winter's Dream Joey Curtis Paul Sidhu, Arielle Holmes, Branden Coles, Kel... United States March 1, 2018 2016 TV-MA 101 min Action & Adventure, Independent Movies, Sci-Fi... In the frozen tundra of a futuristic Arizona w...
69 The Black Room 2016 3.9 Horror 80184868 Movie The Black Room Rolfe Kanefsky Natasha Henstridge, Lukas Hassel, Lin Shaye, D... United States August 7, 2017 2016 TV-MA 95 min Horror Movies A couple's new dream home morphs into a nightm...
3 The Last Face 2016 3.7 Drama 80115030 Movie The Last Face Sean Penn Javier Bardem, Charlize Theron, Adèle Exarchop... United States January 13, 2020 2016 R 131 min Dramas Savage civil war and a dispute over humanitari...
178 The Intent 2016 3.5 Crime,Drama 80178078 Movie The Intent Femi Oyeniran, Kalvadour Peterson Dylan Duffus, Scorcher, Shone Romulus, Jade As... United Kingdom May 15, 2017 2016 TV-MA 99 min Dramas, International Movies, Thrillers After burgeoning criminal Gunz joins the incre...

200 rows × 16 columns

In [62]:
import plotly.express as px
In [63]:
top_rated = joint_data[:10]
In [64]:
px.sunburst(top_rated,path=['title','country'],values='Rating',color='Rating')
In [65]:
country_count = joint_data['country'].value_counts().sort_values(ascending=False)
In [66]:
country_count
Out[66]:
United States                                       89
India                                                9
United Kingdom, United States                        8
United Kingdom                                       8
United States, United Kingdom                        5
                                                    ..
France, United States, Mexico                        1
Mexico, Spain                                        1
United States, Canada, United Kingdom                1
United Kingdom, South Africa                         1
South Africa, United States, New Zealand, Canada     1
Name: country, Length: 68, dtype: int64
In [67]:
country_count = pd.DataFrame(country_count)
In [69]:
country_count.head(10)
Out[69]:
country
United States 89
India 9
United Kingdom, United States 8
United Kingdom 8
United States, United Kingdom 5
Australia 4
Canada 4
Canada, United States 3
United States, Canada 2
United States, China 2
In [76]:
top_countries = country_count[:10]
top_countries.index
Out[76]:
Index(['United States', 'India', 'United Kingdom, United States',
       'United Kingdom', 'United States, United Kingdom', 'Australia',
       'Canada', 'Canada, United States', 'United States, Canada',
       'United States, China'],
      dtype='object')
In [74]:
px.funnel(top_countries)
In [77]:
data = dict(number=[89,9,8,8,5,4,4,3,2,2],
           country=['United States', 'India', 'United Kingdom, United States','United Kingdom', 'United States, United Kingdom', 
                    'Australia','Canada', 'Canada, United States', 'United States, Canada','United States, China'])
In [78]:
px.funnel(data,x='number',y='country')
In [79]:
netflix_movies
Out[79]:
show_id type title director cast country date_added release_year rating duration listed_in description
0 81145628 Movie Norm of the North: King Sized Adventure Richard Finn, Tim Maltby Alan Marriott, Andrew Toth, Brian Dobson, Cole... United States, India, South Korea, China September 9, 2019 2019 TV-PG 90 min Children & Family Movies, Comedies Before planning an awesome wedding for his gra...
1 80117401 Movie Jandino: Whatever it Takes NaN Jandino Asporaat United Kingdom September 9, 2016 2016 TV-MA 94 min Stand-Up Comedy Jandino Asporaat riffs on the challenges of ra...
4 80125979 Movie #realityhigh Fernando Lebrija Nesta Cooper, Kate Walsh, John Michael Higgins... United States September 8, 2017 2017 TV-14 99 min Comedies When nerdy high schooler Dani finally attracts...
6 70304989 Movie Automata Gabe Ibáñez Antonio Banderas, Dylan McDermott, Melanie Gri... Bulgaria, United States, Spain, Canada September 8, 2017 2014 R 110 min International Movies, Sci-Fi & Fantasy, Thrillers In a dystopian future, an insurance adjuster f...
7 80164077 Movie Fabrizio Copano: Solo pienso en mi Rodrigo Toro, Francisco Schultz Fabrizio Copano Chile September 8, 2017 2017 TV-MA 60 min Stand-Up Comedy Fabrizio Copano takes audience participation t...
... ... ... ... ... ... ... ... ... ... ... ... ...
5577 80085438 Movie Frank and Cindy G.J. Echternkamp NaN United States April 1, 2016 2007 TV-MA 70 min Documentaries Frank was a rising pop star when he married Ci...
5578 80085439 Movie Frank and Cindy G.J. Echternkamp Rene Russo, Oliver Platt, Johnny Simmons, Jane... United States April 1, 2016 2015 R 102 min Comedies, Dramas, Independent Movies A student filmmaker vengefully turns his camer...
5579 80011846 Movie Iverson Zatella Beatty Allen Iverson United States April 1, 2016 2014 NR 88 min Documentaries, Sports Movies This unfiltered documentary follows the rocky ...
5580 80064521 Movie Jeremy Scott: The People's Designer Vlad Yudin Jeremy Scott United States April 1, 2016 2015 PG-13 109 min Documentaries The journey of fashion designer Jeremy Scott f...
6231 80116008 Movie Little Baby Bum: Nursery Rhyme Friends NaN NaN NaN NaN 2016 NaN 60 min Movies Nursery rhymes and original music for children...

4265 rows × 12 columns

In [80]:
plt.figure(figsize=(12,10))

sns.countplot(y='release_year',data=netflix_movies,palette='coolwarm')
Out[80]:
<matplotlib.axes._subplots.AxesSubplot at 0x20fbef6d688>
In [81]:
plt.figure(figsize=(12,10))

sns.countplot(y='release_year',data=netflix_movies,palette='coolwarm',order=netflix_movies['release_year'].value_counts().index[:15])
Out[81]:
<matplotlib.axes._subplots.AxesSubplot at 0x20fbf18a808>
In [83]:
netflix_movies['country'].isnull().sum()
Out[83]:
195
In [84]:
netflix_movies.isnull().sum()
Out[84]:
show_id           0
type              0
title             0
director        128
cast            360
country         195
date_added        1
release_year      0
rating            8
duration          0
listed_in         0
description       0
dtype: int64
In [85]:
countries={}
In [86]:
netflix_movies['country'] = netflix_movies['country'].fillna('Unknown')
D:\ram\lib\site-packages\ipykernel_launcher.py:1: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

In [89]:
netflix_movies[netflix_movies['country']=='Unknown']
Out[89]:
show_id type title director cast country date_added release_year rating duration listed_in description
10 80169755 Movie Joaquín Reyes: Una y no más José Miguel Contreras Joaquín Reyes Unknown September 8, 2017 2017 TV-MA 78 min Stand-Up Comedy Comedian and celebrity impersonator Joaquín Re...
12 80182480 Movie Krish Trish and Baltiboy NaN Damandeep Singh Baggan, Smita Malhotra, Baba S... Unknown September 8, 2017 2009 TV-Y7 58 min Children & Family Movies A team of minstrels, including a monkey, cat a...
13 80182483 Movie Krish Trish and Baltiboy: Battle of Wits Munjal Shroff, Tilak Shetty Damandeep Singh Baggan, Smita Malhotra, Baba S... Unknown September 8, 2017 2013 TV-Y7 62 min Children & Family Movies An artisan is cheated of his payment, a lion o...
14 80182596 Movie Krish Trish and Baltiboy: Best Friends Forever Munjal Shroff, Tilak Shetty Damandeep Singh Baggan, Smita Malhotra, Deepak... Unknown September 8, 2017 2016 TV-Y 65 min Children & Family Movies A cat, monkey and donkey team up to narrate fo...
15 80182482 Movie Krish Trish and Baltiboy: Comics of India Tilak Shetty Damandeep Singh Baggan, Smita Malhotra, Baba S... Unknown September 8, 2017 2012 TV-Y7 61 min Children & Family Movies In three comic-strip-style tales, a boy tries ...
... ... ... ... ... ... ... ... ... ... ... ... ...
5374 81035850 Movie My Wife and My Wifey Moataz El Tony Ramez Galal, Shery Adel, Hassan Hosny, Samy Ma... Unknown April 18, 2019 2014 TV-14 99 min Comedies, International Movies A man finds his marriage to a dedicated women'...
5385 81013626 Movie HOMECOMING: A film by Beyoncé Beyoncé Knowles-Carter Beyoncé Knowles-Carter Unknown April 17, 2019 2019 TV-MA 138 min Documentaries, Music & Musicals This intimate, in-depth look at Beyoncé's cele...
5394 80999069 Movie Super Monsters Furever Friends NaN Elyse Maloway, Vincent Tong, Erin Mathews, And... Unknown April 16, 2019 2019 TV-Y 59 min Children & Family Movies On the first night of spring, the Super Monste...
5522 80196139 Movie Fishpeople Keith Malloy NaN Unknown April 1, 2018 2017 TV-14 49 min Documentaries, Sports Movies In this exploration of the life-changing power...
6231 80116008 Movie Little Baby Bum: Nursery Rhyme Friends NaN NaN Unknown NaN 2016 NaN 60 min Movies Nursery rhymes and original music for children...

195 rows × 12 columns

In [91]:
cou = list(netflix_movies['country'])
In [92]:
cou
Out[92]:
['United States, India, South Korea, China',
 'United Kingdom',
 'United States',
 'Bulgaria, United States, Spain, Canada',
 'Chile',
 'United States, United Kingdom, Denmark, Sweden',
 'Unknown',
 'Netherlands, Belgium, United Kingdom, United States',
 'Unknown',
 'Unknown',
 'Unknown',
 'Unknown',
 'Unknown',
 'Unknown',
 'Unknown',
 'France, Belgium',
 'United States',
 'France, Belgium',
 'United States, Uruguay',
 'United States',
 'United States',
 'United States',
 'United States,',
 'Thailand',
 'China, Canada, United States',
 'United States',
 'Belgium, United Kingdom, United States',
 'Belgium, France',
 'India',
 'Unknown',
 'India',
 'Unknown',
 'United States',
 'India',
 'United Kingdom',
 'United Kingdom',
 'Unknown',
 'United States, Canada',
 'Thailand',
 'Thailand',
 'Thailand',
 'Thailand',
 'Thailand',
 'Thailand',
 'United States',
 'United States',
 'Pakistan',
 'Canada',
 'United States',
 'India',
 'United States',
 'United Kingdom, France',
 'United Kingdom',
 'United States, United Kingdom',
 'South Korea',
 'Denmark, United States',
 'United Kingdom, United States',
 'Turkey, United States',
 'Brazil',
 'United States',
 'United States',
 'United States',
 'Unknown',
 'United States',
 'Unknown',
 'Denmark, France, Italy, Belgium, Netherlands',
 'Unknown',
 'Indonesia',
 'China',
 'United States',
 'United States',
 'Indonesia',
 'Indonesia',
 'Indonesia',
 'Spain',
 'Ireland, United Kingdom',
 'Turkey',
 'United States',
 'United States',
 'Hong Kong',
 'France, Morocco',
 'United States',
 'India',
 'Hong Kong, China',
 'United States',
 'United States',
 'United States',
 'United States',
 'United States, Mexico',
 'Vietnam',
 'France, Canada',
 'India',
 'India',
 'United States',
 'United States',
 'United States',
 'United Kingdom',
 'United States',
 'United States',
 'China',
 'Canada',
 'Spain, Argentina',
 'India',
 'United States',
 'United States',
 'United States',
 'India',
 'United States',
 'United Kingdom, United States',
 'United States',
 'United States',
 'United States',
 'Nigeria',
 'Nigeria',
 'France',
 'Nigeria',
 'United States',
 'Brazil',
 'United States',
 'United States',
 'Turkey',
 'Canada',
 'Hong Kong',
 'Hong Kong',
 'United States, Greece, United Kingdom',
 'United States',
 'Vietnam',
 'China',
 'United States',
 'Norway, United Kingdom, France, Ireland',
 'India',
 'France, Switzerland, Spain, United States, United Arab Emirates',
 'United States',
 'United States',
 'Unknown',
 'Unknown',
 'Unknown',
 'Unknown',
 'Unknown',
 'Unknown',
 'Canada, United States, United Kingdom',
 'United States',
 'United States',
 'United States',
 'United States',
 'United Kingdom',
 'United States',
 'Hong Kong',
 'United States',
 'United Kingdom, Canada, United States',
 'United States',
 'United States',
 'United States, United Kingdom, Canada, Japan',
 'Canada, United States',
 'United States',
 'United States',
 'United States',
 'United Kingdom',
 'United States',
 'United States',
 'United States',
 'Indonesia',
 'Ireland',
 'India',
 'Indonesia',
 'Indonesia',
 'India',
 'Unknown',
 'India',
 'India',
 'India',
 'India',
 'Unknown',
 'Cambodia, United States',
 'Russia',
 'Pakistan',
 'United States, Mexico',
 'Mexico',
 'United States, Denmark',
 'United States',
 'United States',
 'United States',
 'India',
 'United States',
 'United States',
 'Thailand',
 'Japan',
 'Thailand',
 'Israel, United States',
 'United States',
 'United States',
 'United States',
 'United States',
 'United States',
 'Turkey',
 'China',
 'United States',
 'United States',
 'United States',
 'United States',
 'United States',
 'United States',
 'Italy',
 'United States',
 'United States',
 'Netherlands',
 'United Kingdom',
 'United States, United Kingdom',
 'United States',
 'Unknown',
 'United Kingdom',
 'United States',
 'United States',
 'United States, Canada',
 'United States',
 'Brazil, United States',
 'United Kingdom, Canada, United States',
 'United States',
 'United States, France',
 'Germany, United States, Canada',
 'Nigeria',
 'United States',
 'United States',
 'United States, Australia',
 'Nigeria',
 'Denmark, Brazil, France, Portugal, Sweden',
 'United States',
 'Nigeria',
 'United States',
 'United States',
 'United States',
 'United States',
 'United States',
 'Nigeria',
 'Nigeria',
 'United States',
 'France, United States',
 'United States',
 'United States, United Kingdom',
 'United Arab Emirates',
 'Egypt',
 'United States',
 'Spain',
 'United States',
 'France, Belgium',
 'United States',
 'India',
 'India',
 'India',
 'India',
 'United States',
 'India, Germany, Austria',
 'Thailand',
 'Mexico',
 'France',
 'United States',
 'United States',
 'United States',
 'Unknown',
 'Thailand',
 'United States',
 'Unknown',
 'India',
 'Unknown',
 'Japan',
 'Japan',
 'United States',
 'India',
 'Czech Republic, France',
 'India',
 'United States, Senegal',
 'Japan',
 'Japan',
 'Japan',
 'India',
 'United States',
 'India',
 'India',
 'India',
 'Japan',
 'Japan',
 'Japan',
 'Japan',
 'Japan',
 'Japan',
 'Germany',
 'United States',
 'France',
 'United States',
 'India, Soviet Union',
 'Argentina, Spain',
 'United States, Hong Kong',
 'China',
 'United Kingdom, Italy, Israel, Peru, United States',
 'Argentina, Uruguay, Spain, France',
 'Argentina',
 'United States',
 'Netherlands',
 'Netherlands',
 'Pakistan, Norway, United States',
 'United Kingdom',
 'Canada, United States',
 'United States',
 'Spain',
 'United Kingdom',
 'Spain',
 'United States',
 'United States',
 'United States',
 'Indonesia',
 'United States',
 'Indonesia',
 'United Kingdom, Ukraine, United States',
 'United States',
 'United States',
 'Malaysia',
 'Australia, United States',
 'United States',
 'United States',
 'United States',
 'Ireland, South Africa',
 'Indonesia',
 'Vietnam',
 'United States',
 'China, India, Nepal',
 'United States',
 'India',
 'Indonesia',
 'Indonesia',
 'United Kingdom',
 'United States',
 'Unknown',
 'United Kingdom, Hong Kong',
 'Turkey',
 'Canada',
 'Turkey',
 'United States',
 'United Kingdom, Canada, Italy',
 'Indonesia',
 'United States',
 'United States',
 'Argentina',
 'Spain, France',
 'Philippines',
 'United States',
 'United States',
 'United States',
 'India',
 'India',
 'India',
 'India',
 'Indonesia',
 'United States',
 'United States',
 'United States',
 'United Kingdom, India, United States',
 'United States',
 'United States',
 'United States',
 'United States',
 'United States',
 'United States',
 'France, Belgium, China, United States',
 'Canada',
 'Mexico, United States',
 'United Kingdom',
 'United States, Indonesia',
 'Canada',
 'Canada, United States',
 'United Kingdom, Germany, Canada',
 'France',
 'France, Belgium',
 'France',
 'United States',
 'Hong Kong, China',
 'United States',
 'Spain',
 'United States',
 'United States',
 'Unknown',
 'Russia, Poland, Serbia',
 'Serbia, United States',
 'Spain, Portugal',
 'Colombia',
 'United States, United Kingdom',
 'India',
 'United States',
 'Israel, United States',
 'United States',
 'Turkey',
 'United States',
 'United States, United Kingdom, Germany',
 'Unknown',
 'United States',
 'Switzerland',
 'United States',
 'United Kingdom',
 'United States',
 'United States',
 'United States',
 'Singapore, Malaysia',
 'India',
 'United Kingdom',
 'United States',
 'Canada, Luxembourg',
 'United States',
 'Hong Kong, China',
 'United States',
 'United States',
 'United States',
 'United States, Spain, Germany',
 'United States',
 'United States',
 'United States',
 'United States',
 'Egypt, Austria, United States',
 'United States',
 'India',
 'India',
 'India',
 'India',
 'India',
 'India',
 'India',
 'United States',
 'United Kingdom',
 'Indonesia',
 'United States',
 'United States',
 'Peru',
 'United Kingdom, United States, Morocco',
 'United States',
 'United States',
 'United States',
 'United States',
 'Mexico',
 'United States',
 'United States, Bulgaria',
 'United States',
 'Unknown',
 'Unknown',
 'United Kingdom',
 'United Kingdom',
 'United States',
 'United Kingdom, United States',
 'United Kingdom',
 'United Kingdom',
 'United Kingdom',
 'United States',
 'United States',
 'United States',
 'Unknown',
 'United States',
 'Nigeria',
 'Belgium, Luxembourg, France',
 'Mexico, Argentina',
 'Unknown',
 'United Kingdom, Canada, United States, Cayman Islands',
 'South Korea',
 'India',
 'Unknown',
 'Hong Kong, China',
 'United States, United Kingdom, Morocco',
 'Unknown',
 'Unknown',
 'Indonesia, United States',
 'France',
 'South Africa',
 'Nigeria',
 'United States',
 'Unknown',
 'Turkey',
 'Nigeria',
 'Spain',
 'Nigeria',
 'United Kingdom',
 'United States',
 'Nigeria',
 'India',
 'Indonesia',
 'India',
 'Unknown',
 'United States, China',
 'Canada',
 'United States',
 'United States',
 'United States',
 'South Korea',
 'United States, United Kingdom',
 'United States',
 'United States',
 'India',
 'United Kingdom, United States',
 'India',
 'Netherlands, Denmark, South Africa',
 'United States',
 'United States',
 'United States',
 'United Kingdom, Poland, United States',
 'Unknown',
 'India',
 'India',
 'India',
 'India',
 'India',
 'New Zealand',
 'India',
 'India',
 'Venezuela',
 'India',
 'India',
 'India',
 'United States, Spain',
 'India',
 'Australia, United Arab Emirates',
 'Australia, India',
 'India, Malaysia',
 'India',
 'India',
 'India',
 'India',
 'India',
 'Canada, India, Thailand, United States, United Arab Emirates',
 'France',
 'Japan',
 'Japan',
 'Japan',
 'France',
 'Japan',
 'Japan',
 'Canada',
 'China',
 'United States',
 'Italy, France',
 'United States',
 'France',
 'India',
 'Germany, Jordan, Netherlands',
 'Turkey, France, Germany, Poland',
 'Mexico',
 'United Kingdom',
 'United States',
 'United States',
 'United States',
 'United Kingdom, United States',
 'Spain, France',
 'United States',
 'Indonesia',
 'United States',
 'United States, Israel, United Kingdom, Canada',
 'Saudi Arabia',
 'Spain',
 'United States',
 'United States',
 'Turkey',
 'United States',
 'United States',
 'Nigeria',
 'Japan',
 'Spain, France',
 'Indonesia',
 'Egypt, France',
 'Unknown',
 'Norway, Iceland, United States',
 'United States',
 'United States',
 'United States',
 'Denmark, France, Poland',
 'Canada',
 'United States, Germany, Canada',
 'United States',
 'United States',
 'United States',
 'United States',
 'United States, United Kingdom',
 'United States, Germany',
 'United States',
 'United States, Germany',
 'United States',
 'United States',
 'Poland',
 'United States',
 'Netherlands',
 'United States',
 'United States, Canada',
 'United States',
 'United States',
 'Poland,',
 'United States',
 'Poland',
 'United States, United Kingdom, Australia',
 'India',
 'Unknown',
 'Poland, West Germany',
 'United States',
 'United States',
 'United States',
 'United States',
 'United States',
 'United States',
 'United States',
 'Germany, United States',
 'United States',
 'United Kingdom, France, United States',
 'United States',
 'Peru',
 'United Kingdom, United States',
 'United States',
 'United States',
 'United States',
 'United States',
 'Poland',
 'Nigeria',
 'United States',
 'United States',
 'Poland',
 'United States',
 'United States',
 'Poland',
 'United States',
 'United States',
 'United Kingdom',
 'United States, Malta, United Kingdom',
 'United States',
 'United States',
 'Poland',
 'Poland',
 'India',
 'Canada',
 'India',
 'India',
 'India',
 'United States',
 'United States, Sweden',
 'India',
 'India',
 'India',
 'Unknown',
 'India',
 'United States',
 'Unknown',
 'India',
 'India',
 'India',
 'India',
 'India',
 'India',
 'India',
 'India',
 'United States',
 'United States',
 'India',
 'India',
 'India',
 'India',
 'India',
 'India',
 'India',
 'India',
 'India',
 'India',
 'Australia',
 'India',
 'India',
 'India',
 'United States',
 'United States, Canada',
 'United States',
 'India',
 'India',
 'India',
 'India',
 'India, United States',
 'India',
 'India',
 'India',
 'India',
 'India',
 'India',
 'India',
 'India',
 'Unknown',
 'India',
 'India',
 'India',
 'United States, United Kingdom, Germany',
 'India',
 'India',
 'India, Australia',
 'India',
 'United States',
 'Pakistan',
 'South Korea',
 'France, Canada, Belgium',
 'United States',
 'United States',
 'United States',
 'United States',
 'United States',
 'United States',
 'United States, Canada',
 'United States, Canada',
 'United States',
 'United States',
 'Brazil',
 'Spain',
 'United States, Italy',
 'United States, Brazil',
 'United States',
 'Canada',
 'United Kingdom',
 'United States',
 'Canada, Ireland, United States',
 'United States, France, Canada, Lebanon, Qatar',
 'Japan',
 'United States',
 'United States',
 'Switzerland, France',
 'France, Belgium',
 'New Zealand',
 'Norway, Germany',
 'South Korea',
 'United Kingdom, Canada, Japan',
 'Mexico',
 'United States',
 'India',
 'Unknown',
 'Brazil',
 'India',
 'Chile, United States, France',
 'Spain',
 'United States, Canada',
 'Unknown',
 'Netherlands',
 'India',
 'United States',
 'Italy',
 'United States',
 'United States',
 'United States',
 'United States',
 'United States',
 'United States',
 'United States',
 'United States',
 'United States',
 'United States',
 'United States',
 'Hong Kong',
 'Brazil, France',
 'Germany',
 'United Kingdom, United States',
 'United States',
 'United States',
 'France',
 'United States',
 'Argentina, Chile',
 'United States',
 'Thailand',
 'Thailand',
 'Italy',
 'India',
 'Canada',
 'United States',
 'United States',
 'United States',
 'United Kingdom,',
 'United States',
 'China, Hong Kong',
 'United States',
 'Unknown',
 'United States',
 'United States',
 'South Korea',
 'United States',
 'India',
 'United States',
 'India',
 'United States',
 'Canada, United States',
 'Canada',
 'South Africa, United States, New Zealand, Canada',
 'United States',
 'United States',
 'India',
 'United Kingdom',
 'United States',
 'Austria',
 'Unknown',
 'India',
 'United States',
 'Ireland, Canada',
 'Mexico',
 'Indonesia',
 'Italy, Switzerland, France, Germany',
 'Indonesia',
 'India',
 'India',
 'France',
 'Mexico, Netherlands',
 'Peru, United States, United Kingdom',
 'United States',
 'United States',
 'France, Senegal, Belgium',
 'Nigeria',
 'France',
 'South Africa',
 'Unknown',
 'Nigeria',
 'Nigeria',
 'Nigeria',
 'United States',
 'Germany, Canada, United States',
 'China',
 'United States',
 'India',
 'Unknown',
 'Canada, Norway',
 'United States',
 'China, Morocco, Hong Kong',
 'India',
 'United States',
 'United States',
 'Unknown',
 'India',
 'Unknown',
 'United States',
 'United States',
 'Uruguay',
 'Mexico',
 'United States',
 'United States',
 'United States',
 'United States',
 'United States',
 'United States',
 'Spain',
 'United States',
 'United States',
 'United States',
 'United States, United Arab Emirates',
 'Spain, Belgium, Switzerland, United States, China, United Kingdom',
 'France',
 'Japan',
 'United States',
 'United States',
 'United States',
 'United States',
 'India',
 'United States',
 'India',
 'United Kingdom, Germany, Canada, United States',
 'United States',
 'United States',
 'Spain',
 'Australia, Canada',
 'United States',
 'United States',
 'United States',
 'Mexico',
 'Australia, France',
 'United States',
 'United States',
 'United States',
 'United States',
 'United States',
 'United States',
 'India',
 'United States',
 'United States',
 'United States',
 'United States',
 'United States',
 'Mexico',
 'Germany, United Kingdom',
 'India',
 'Italy, United States',
 'United States',
 'United States',
 'United States, New Zealand, United Kingdom',
 'United States',
 'United States',
 'United States',
 'United States',
 'United States',
 'United Kingdom, Germany, United States',
 'United States, Germany',
 'United States',
 'United States',
 'United States',
 'United States',
 'United States',
 'United States',
 'Indonesia',
 'Indonesia',
 'United States',
 'United States',
 'United States',
 'United States, Canada',
 'United States, Australia, Mexico',
 'United States',
 'United States, South Korea, Japan',
 'United States, Canada',
 'United States',
 'United States',
 'United States',
 'United States, Canada',
 'United States',
 'Brazil',
 'United States',
 'United States',
 'France, Iran, United States',
 'United States',
 'United Kingdom',
 'United Kingdom, Australia, Canada, United States',
 'United States',
 'United States',
 'United States',
 'South Korea',
 'United States',
 'France, Qatar',
 'United Kingdom',
 'United States',
 'United States',
 'United States',
 'Canada, United States',
 'India',
 'United States',
 'United States',
 'United States',
 'Finland, Germany, Belgium',
 'United States',
 'United Kingdom, France',
 'United States, Spain, Chile, Peru',
 'United States',
 'United Arab Emirates, United States, United Kingdom',
 'France, Belgium',
 'United States',
 'United States',
 'United States',
 'United States',
 'Colombia',
 'Argentina',
 'India',
 'India',
 'Japan',
 'Spain',
 'United States, Ireland',
 'India, United States',
 'United States',
 'Japan',
 'India',
 'Canada',
 'India',
 'India',
 'Indonesia',
 'India',
 'India',
 'Australia, Iraq',
 'India',
 'India',
 'United States',
 'Australia',
 'Australia',
 'France',
 'Spain',
 'India',
 'India',
 'Germany',
 'India',
 'United States',
 'France',
 'United States',
 'United States',
 'United States',
 'United States, Mexico',
 'Philippines',
 'Unknown',
 'United States',
 'United States',
 'United States',
 'United States',
 'Canada',
 'United States',
 'United States',
 'United States',
 'Canada',
 'United States',
 'France',
 'United States',
 'Mexico',
 'United States',
 'Brazil',
 'United States',
 'United States, Germany',
 'United States',
 'Spain',
 'United States',
 'United States',
 'United States',
 ...]
In [93]:
for i in cou:
    i = list(i.split(','))
    if len(i)==1:
        if i in list(countries.keys()):
            countries[i]+=1
        else:
            countries[i[0]]=1
    else:
        for j in i:
            if j in list(countries.keys()):
                countries[j]+1
            else:
                countries[j]=1
In [95]:
countries
Out[95]:
{'United States': 1,
 ' India': 1,
 ' South Korea': 1,
 ' China': 1,
 'United Kingdom': 1,
 'Bulgaria': 1,
 ' United States': 1,
 ' Spain': 1,
 ' Canada': 1,
 'Chile': 1,
 ' United Kingdom': 1,
 ' Denmark': 1,
 ' Sweden': 1,
 'Unknown': 1,
 'Netherlands': 1,
 ' Belgium': 1,
 'France': 1,
 ' Uruguay': 1,
 '': 1,
 'Thailand': 1,
 'China': 1,
 'Belgium': 1,
 ' France': 1,
 'India': 1,
 'Pakistan': 1,
 'Canada': 1,
 'South Korea': 1,
 'Denmark': 1,
 'Turkey': 1,
 'Brazil': 1,
 ' Italy': 1,
 ' Netherlands': 1,
 'Indonesia': 1,
 'Spain': 1,
 'Ireland': 1,
 'Hong Kong': 1,
 ' Morocco': 1,
 ' Mexico': 1,
 'Vietnam': 1,
 ' Argentina': 1,
 'Nigeria': 1,
 ' Greece': 1,
 'Norway': 1,
 ' Ireland': 1,
 ' Switzerland': 1,
 ' United Arab Emirates': 1,
 ' Japan': 1,
 'Cambodia': 1,
 'Russia': 1,
 'Mexico': 1,
 'Japan': 1,
 'Israel': 1,
 'Italy': 1,
 'Germany': 1,
 ' Australia': 1,
 ' Brazil': 1,
 ' Portugal': 1,
 'United Arab Emirates': 1,
 'Egypt': 1,
 ' Germany': 1,
 ' Austria': 1,
 'Czech Republic': 1,
 ' Senegal': 1,
 ' Soviet Union': 1,
 'Argentina': 1,
 ' Hong Kong': 1,
 ' Israel': 1,
 ' Peru': 1,
 ' Norway': 1,
 ' Ukraine': 1,
 'Malaysia': 1,
 'Australia': 1,
 ' South Africa': 1,
 ' Nepal': 1,
 'Philippines': 1,
 ' Indonesia': 1,
 ' Poland': 1,
 ' Serbia': 1,
 'Serbia': 1,
 'Colombia': 1,
 'Switzerland': 1,
 'Singapore': 1,
 ' Malaysia': 1,
 ' Luxembourg': 1,
 'Peru': 1,
 ' Bulgaria': 1,
 ' Cayman Islands': 1,
 'South Africa': 1,
 'New Zealand': 1,
 'Venezuela': 1,
 ' Thailand': 1,
 ' Jordan': 1,
 'Saudi Arabia': 1,
 ' Iceland': 1,
 'Poland': 1,
 ' West Germany': 1,
 ' Malta': 1,
 ' Lebanon': 1,
 ' Qatar': 1,
 ' Chile': 1,
 ' New Zealand': 1,
 'Austria': 1,
 'Uruguay': 1,
 ' Iran': 1,
 'Finland': 1,
 ' Iraq': 1,
 ' Liechtenstein': 1,
 'Taiwan': 1,
 ' Albania': 1,
 ' Russia': 1,
 ' Pakistan': 1,
 ' Slovakia': 1,
 ' Czech Republic': 1,
 ' Samoa': 1,
 'Ghana': 1,
 ' Cambodia': 1,
 ' Finland': 1,
 'Iceland': 1,
 ' Colombia': 1,
 ' Botswana': 1,
 'Iran': 1,
 ' Taiwan': 1,
 'Sweden': 1,
 'Hungary': 1,
 'Guatemala': 1,
 'Portugal': 1,
 ' Malawi': 1,
 'Paraguay': 1,
 'Somalia': 1,
 ' Kenya': 1,
 ' Sudan': 1,
 ' Sri Lanka': 1,
 'Dominican Republic': 1,
 ' Panama': 1,
 'Romania': 1,
 ' Latvia': 1,
 ' Singapore': 1,
 ' Uganda': 1,
 'Slovenia': 1,
 ' Croatia': 1,
 'Croatia': 1,
 ' Slovenia': 1,
 ' Montenegro': 1,
 'Bangladesh': 1,
 ' Vatican City': 1,
 ' Egypt': 1,
 'Soviet Union': 1,
 'Lebanon': 1,
 ' Dominican Republic': 1,
 ' East Germany': 1,
 ' Bangladesh': 1,
 ' Afghanistan': 1,
 ' Venezuela': 1,
 'Georgia': 1,
 ' Namibia': 1,
 ' Zimbabwe': 1,
 'West Germany': 1,
 ' Hungary': 1,
 ' Nicaragua': 1,
 ' Romania': 1,
 ' Kazakhstan': 1,
 ' Turkey': 1,
 ' Armenia': 1,
 ' Mongolia': 1,
 ' Philippines': 1,
 ' Bermuda': 1,
 ' Ecuador': 1}
In [96]:
countries_fin={}
In [97]:
for country,no in countries.items():
    country = country.replace('','')
    if country in list(countries_fin.keys()):
        countries_fin[country]+=no
    else:
        countries_fin[country]=no
        
countries_fin = {k:v for k, v in sorted(countries_fin.items(),key=lambda item: item[1],reverse=True)}
In [100]:
countries_fin.keys()
Out[100]:
dict_keys(['United States', ' India', ' South Korea', ' China', 'United Kingdom', 'Bulgaria', ' United States', ' Spain', ' Canada', 'Chile', ' United Kingdom', ' Denmark', ' Sweden', 'Unknown', 'Netherlands', ' Belgium', 'France', ' Uruguay', '', 'Thailand', 'China', 'Belgium', ' France', 'India', 'Pakistan', 'Canada', 'South Korea', 'Denmark', 'Turkey', 'Brazil', ' Italy', ' Netherlands', 'Indonesia', 'Spain', 'Ireland', 'Hong Kong', ' Morocco', ' Mexico', 'Vietnam', ' Argentina', 'Nigeria', ' Greece', 'Norway', ' Ireland', ' Switzerland', ' United Arab Emirates', ' Japan', 'Cambodia', 'Russia', 'Mexico', 'Japan', 'Israel', 'Italy', 'Germany', ' Australia', ' Brazil', ' Portugal', 'United Arab Emirates', 'Egypt', ' Germany', ' Austria', 'Czech Republic', ' Senegal', ' Soviet Union', 'Argentina', ' Hong Kong', ' Israel', ' Peru', ' Norway', ' Ukraine', 'Malaysia', 'Australia', ' South Africa', ' Nepal', 'Philippines', ' Indonesia', ' Poland', ' Serbia', 'Serbia', 'Colombia', 'Switzerland', 'Singapore', ' Malaysia', ' Luxembourg', 'Peru', ' Bulgaria', ' Cayman Islands', 'South Africa', 'New Zealand', 'Venezuela', ' Thailand', ' Jordan', 'Saudi Arabia', ' Iceland', 'Poland', ' West Germany', ' Malta', ' Lebanon', ' Qatar', ' Chile', ' New Zealand', 'Austria', 'Uruguay', ' Iran', 'Finland', ' Iraq', ' Liechtenstein', 'Taiwan', ' Albania', ' Russia', ' Pakistan', ' Slovakia', ' Czech Republic', ' Samoa', 'Ghana', ' Cambodia', ' Finland', 'Iceland', ' Colombia', ' Botswana', 'Iran', ' Taiwan', 'Sweden', 'Hungary', 'Guatemala', 'Portugal', ' Malawi', 'Paraguay', 'Somalia', ' Kenya', ' Sudan', ' Sri Lanka', 'Dominican Republic', ' Panama', 'Romania', ' Latvia', ' Singapore', ' Uganda', 'Slovenia', ' Croatia', 'Croatia', ' Slovenia', ' Montenegro', 'Bangladesh', ' Vatican City', ' Egypt', 'Soviet Union', 'Lebanon', ' Dominican Republic', ' East Germany', ' Bangladesh', ' Afghanistan', ' Venezuela', 'Georgia', ' Namibia', ' Zimbabwe', 'West Germany', ' Hungary', ' Nicaragua', ' Romania', ' Kazakhstan', ' Turkey', ' Armenia', ' Mongolia', ' Philippines', ' Bermuda', ' Ecuador'])
In [103]:
plt.figure(figsize=(12,7))

ax = sns.barplot(x=list(countries_fin.keys())[:10],y=list(countries_fin.values())[:10])
ax.set_xticklabels(list(countries_fin.keys())[0:10],rotation = 90)
Out[103]:
[Text(0, 0, 'United States'),
 Text(0, 0, ' India'),
 Text(0, 0, ' South Korea'),
 Text(0, 0, ' China'),
 Text(0, 0, 'United Kingdom'),
 Text(0, 0, 'Bulgaria'),
 Text(0, 0, ' United States'),
 Text(0, 0, ' Spain'),
 Text(0, 0, ' Canada'),
 Text(0, 0, 'Chile')]
In [105]:
netflix_movies['duration'].value_counts()
Out[105]:
90 min     111
91 min     104
92 min     101
94 min      94
95 min      94
          ... 
191 min      1
20 min       1
209 min      1
228 min      1
18 min       1
Name: duration, Length: 186, dtype: int64
In [106]:
netflix_movies['duration'] = netflix_movies['duration'].str.replace('min','')
D:\ram\lib\site-packages\ipykernel_launcher.py:1: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

In [108]:
netflix_movies['duration'][0]
Out[108]:
'90 '
In [109]:
netflix_movies['duration'] = netflix_movies['duration'].astype(str).astype(int)
D:\ram\lib\site-packages\ipykernel_launcher.py:1: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

In [111]:
netflix_movies['duration'][0]
Out[111]:
90
In [112]:
sns.kdeplot(netflix_movies['duration'],shade=True)
Out[112]:
<matplotlib.axes._subplots.AxesSubplot at 0x20fc0320748>
In [113]:
from collections import Counter
In [114]:
genres = list(netflix_movies['listed_in'])
gen = []


for i in genres:
    i = list(i.split(','))
    for j in i:
        gen.append(j.replace('',""))
        
g = Counter(gen)
In [115]:
g
Out[115]:
Counter({'Children & Family Movies': 358,
         ' Comedies': 310,
         'Stand-Up Comedy': 273,
         'Comedies': 803,
         'International Movies': 85,
         ' Sci-Fi & Fantasy': 183,
         ' Thrillers': 352,
         'Action & Adventure': 597,
         ' Dramas': 546,
         ' International Movies': 1842,
         'Cult Movies': 10,
         ' Independent Movies': 534,
         ' Romantic Movies': 374,
         'Documentaries': 644,
         'Horror Movies': 205,
         'Dramas': 1077,
         ' Music & Musicals': 231,
         'Anime Features': 12,
         ' Faith & Spirituality': 47,
         ' Horror Movies': 57,
         'Independent Movies': 18,
         ' LGBTQ Movies': 60,
         ' Cult Movies': 45,
         'Movies': 56,
         'Thrillers': 40,
         'Classic Movies': 62,
         ' Sports Movies': 156,
         ' Anime Features': 33,
         ' Stand-Up Comedy': 8,
         ' Documentaries': 24,
         'Music & Musicals': 12,
         'Sci-Fi & Fantasy': 10,
         ' Children & Family Movies': 20,
         ' Classic Movies': 22,
         'Sports Movies': 1,
         'Romantic Movies': 2})
In [116]:
from wordcloud import WordCloud, STOPWORDS, ImageColorGenerator
In [117]:
text = list(set(gen))
In [118]:
text
Out[118]:
[' Faith & Spirituality',
 'Sci-Fi & Fantasy',
 'Music & Musicals',
 ' Cult Movies',
 'Independent Movies',
 ' Stand-Up Comedy',
 ' International Movies',
 ' Dramas',
 'Sports Movies',
 'International Movies',
 ' Independent Movies',
 'Thrillers',
 ' Music & Musicals',
 'Romantic Movies',
 ' Horror Movies',
 'Dramas',
 ' Classic Movies',
 'Comedies',
 ' Children & Family Movies',
 ' Sports Movies',
 ' Comedies',
 ' Sci-Fi & Fantasy',
 'Cult Movies',
 'Movies',
 ' LGBTQ Movies',
 'Stand-Up Comedy',
 'Action & Adventure',
 'Classic Movies',
 ' Romantic Movies',
 ' Documentaries',
 'Anime Features',
 ' Anime Features',
 'Documentaries',
 'Children & Family Movies',
 'Horror Movies',
 ' Thrillers']
In [121]:
plt.figure(figsize=(20,20))
sns.set_style('white')
wordcloud = WordCloud(max_font_size=50,max_words=100,background_color='white').generate(str(text))


plt.imshow(wordcloud)
Out[121]:
<matplotlib.image.AxesImage at 0x20fbe686b48>
In [120]:
plt.figure(figsize=(20,20))

wordcloud = WordCloud(max_font_size=50,max_words=100,background_color='white').generate(str(text))


plt.imshow(wordcloud,interpolation='bilinear')
Out[120]:
<matplotlib.image.AxesImage at 0x20fbe686d08>
In [123]:
g.keys()
Out[123]:
dict_keys(['Children & Family Movies', ' Comedies', 'Stand-Up Comedy', 'Comedies', 'International Movies', ' Sci-Fi & Fantasy', ' Thrillers', 'Action & Adventure', ' Dramas', ' International Movies', 'Cult Movies', ' Independent Movies', ' Romantic Movies', 'Documentaries', 'Horror Movies', 'Dramas', ' Music & Musicals', 'Anime Features', ' Faith & Spirituality', ' Horror Movies', 'Independent Movies', ' LGBTQ Movies', ' Cult Movies', 'Movies', 'Thrillers', 'Classic Movies', ' Sports Movies', ' Anime Features', ' Stand-Up Comedy', ' Documentaries', 'Music & Musicals', 'Sci-Fi & Fantasy', ' Children & Family Movies', ' Classic Movies', 'Sports Movies', 'Romantic Movies'])
In [124]:
g.values()
Out[124]:
dict_values([358, 310, 273, 803, 85, 183, 352, 597, 546, 1842, 10, 534, 374, 644, 205, 1077, 231, 12, 47, 57, 18, 60, 45, 56, 40, 62, 156, 33, 8, 24, 12, 10, 20, 22, 1, 2])
In [126]:
list(g.values())
Out[126]:
[358,
 310,
 273,
 803,
 85,
 183,
 352,
 597,
 546,
 1842,
 10,
 534,
 374,
 644,
 205,
 1077,
 231,
 12,
 47,
 57,
 18,
 60,
 45,
 56,
 40,
 62,
 156,
 33,
 8,
 24,
 12,
 10,
 20,
 22,
 1,
 2]
In [129]:
x = list(g.keys())
y = list(g.values())
In [131]:
plt.figure(figsize=(30,30))

plt.vlines(x,ymax=0,ymin=y)
Out[131]:
<matplotlib.collections.LineCollection at 0x20fbfe18648>
In [132]:
plt.figure(figsize=(20,20))

sns.barplot(y=list(countries_fin.keys()),x=list(countries_fin.values()))
Out[132]:
<matplotlib.axes._subplots.AxesSubplot at 0x20fc02a44c8>
In [134]:
netflix['description'].describe()
Out[134]:
count                                                  6234
unique                                                 6226
top       A surly septuagenarian gets another chance at ...
freq                                                      3
Name: description, dtype: object
In [ ]: